

import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__A			:					int						=			logging.get_logger(__name__)

__A			:					str						=			[
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]





def 	__a     (      A__			:  Optional[Any]       ):
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        SCREAMING_SNAKE_CASE										=   k.replace(A__			,  A__       )

    if k.startswith("encoder"       ):
        SCREAMING_SNAKE_CASE										=   k.replace(".attn"			,  ".self_attn"       )
        SCREAMING_SNAKE_CASE										=   k.replace("norm1"			,  "self_attn_layer_norm"       )
        SCREAMING_SNAKE_CASE										=   k.replace("norm2"			,  "final_layer_norm"       )
    elif k.startswith("decoder"       ):
        SCREAMING_SNAKE_CASE										=   k.replace("norm1"			,  "self_attn_layer_norm"       )
        SCREAMING_SNAKE_CASE										=   k.replace("norm2"			,  "encoder_attn_layer_norm"       )
        SCREAMING_SNAKE_CASE										=   k.replace("norm3"			,  "final_layer_norm"       )
    return k





def 	__a     (      A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   [
        "model.encoder.layernorm_embedding.weight",
        "model.encoder.layernorm_embedding.bias",
        "model.decoder.layernorm_embedding.weight",
        "model.decoder.layernorm_embedding.bias",
    ]
    for k in keys:
        SCREAMING_SNAKE_CASE										=   sd.pop(A__       )
        SCREAMING_SNAKE_CASE										=   k.replace("layernorm_embedding"			,  "layer_norm"       )
        assert new_k not in sd
        SCREAMING_SNAKE_CASE										=   v


__A			:					Tuple						=			['START']





@torch.no_grad()
def 	__a     (      A__			:  List[Any]			,  A__			:  Union[str, Any]			,  A__			:  Any       ):
    SCREAMING_SNAKE_CASE										=   torch.load(A__			,  map_location="cpu"       )
    SCREAMING_SNAKE_CASE										=   model["model"]
    SCREAMING_SNAKE_CASE										=   BlenderbotConfig.from_json_file(A__       )
    SCREAMING_SNAKE_CASE										=   BlenderbotForConditionalGeneration(A__       )
    SCREAMING_SNAKE_CASE										=   m.model.state_dict().keys()
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE										=   {}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        SCREAMING_SNAKE_CASE										=   rename_state_dict_key(A__       )
        if new_k not in valid_keys:
            failures.append([k, new_k]       )
        else:
            SCREAMING_SNAKE_CASE										=   v
    if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
        rename_layernorm_keys(A__       )
    m.model.load_state_dict(A__			,  strict=A__       )
    m.half()
    m.save_pretrained(A__       )


if __name__ == "__main__":
    __A			:					Optional[int]						=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
    parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
    parser.add_argument(
        '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
    )
    __A			:					Optional[int]						=			parser.parse_args()
    convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case       ):
         snake_case											=							n
         snake_case											=							[None] * self.n
         snake_case											=							0  # index of the first element
         snake_case											=							0
         snake_case											=							0
    def __len__(			self       ):
         return self.size
    def 						a_     (			self       ):
         return self.size == 0
    def 						a_     (			self       ):
         return False if self.is_empty() else self.array[self.front]
    def 						a_     (			self     ,   __snake_case       ):
         if self.size >= self.n:
              raise Exception('''QUEUE IS FULL'''       )

         snake_case											=							data
         snake_case											=							(self.rear + 1) % self.n
         self.size += 1
         return self




    def 						a_     (			self       ):
         if self.size == 0:
              raise Exception('''UNDERFLOW'''       )

         snake_case											=							self.array[self.front]
         snake_case											=							None
         snake_case											=							(self.front + 1) % self.n
         self.size -= 1
         return temp




